Interpretability For Industry 4 0 Statistical And Machine Learning Approaches
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Interpretability for Industry 4.0 : Statistical and Machine Learning Approaches
This volume provides readers with a compact, stimulating and multifaceted introduction to interpretability, a key issue for developing insightful statistical and machine learning approaches as well as for communicating modelling results in business and industry. Different views in the context of Industry 4.0 are offered in connection with the concepts of explainability of machine learning tools, generalizability of model outputs and sensitivity analysis. Moreover, the book explores the integration of Artificial Intelligence and robust analysis of variance for big data mining and monitoring in Additive Manufacturing, and sheds new light on interpretability via random forests and flexible generalized additive models together with related software resources and real-world examples.
A Decade of Research Activities at the Department of Industrial Engineering (UniNa-DII)
This open access book celebrates the decennial of the Department of Industrial Engineering of Università di Napoli Federico II, Italy. It covers the main research achievements developed at the department in the fields of aerospace, marine, energy, statistical, mechanical and management engineering. Five pre-existing departments merged in 2013, and the research results are here summarized to certify how important it was to join skills, expertise, and projects. The industrial engineering area is huge, but it is now dominated by the need to conceive and analyze new solutions, human and climate oriented to face with the actual challenges which dictate the new paradigm, which evolved from “is it feasible?” to “is it compatible with the environment and the human beings?”. There is still a lot to do, but the contents of this book demonstrate that the first steps have been done. All the researchers of the department have contributed to this book, more than 140 authors, and thus, it isthe collective outcome of the path they were able to perform all together, including administrative officers and technicians. It highlights the international relevance and multidisciplinarity of research at the university as well as the planned research lines for the next years.
Quality in the Era of Industry 4.0
QUALITY IN THE ERA OF INDUSTRY 4.0 Enables readers to use real-world data from connected devices to improve product performance, detect design vulnerabilities, and design better solutions Quality in the Era of Industry 4.0 provides an insightful guide to harnessing user performance and behavior data through AI and other Industry 4.0 technologies. This transformative approach enables companies to not only optimize products and services in real-time, but also to anticipate and mitigate likely failures proactively. In a succinct and lucid style, the book presents a pioneering framework for a new paradigm of quality management in the Industry 4.0 landscape. It introduces groundbreaking techniques such as utilizing real-world data to tailor products for superior fit and performance, leveraging connectivity to adapt products to evolving needs and use-cases, and employing cutting-edge manufacturing methods to create bespoke, cost-effective solutions with greater efficiency. Case examples featuring applications from the automotive, mobile device, home appliance, and healthcare industries are used to illustrate how these new quality approaches can be used to benchmark the product’s performance and durability, maintain smart manufacturing, and detect design vulnerabilities. Written by a seasoned expert with experience teaching quality management in both corporate and academic settings, Quality in the Era of Industry 4.0 covers topics such as: Evolution of quality through industrial revolutions, from ancient times to the first and second industrial revolutions Quality by customer value creation, explaining differences in producers, stakeholders, and customers in the new digital age, along with new realities brought by Industry 4.0 Data quality dimensions and strategy, data governance, and new talents and skill sets for quality professionals in Industry 4.0 Automated product lifecycle management, predictive quality control, and defect prevention using technologies like smart factories, IoT, and sensors Quality in the Era of Industry 4.0 is a highly valuable resource for product engineers, quality managers, quality engineers, quality consultants, industrial engineers, and systems engineers who wish to make a participatory approach towards data-driven design, economical mass-customization, and late differentiation.